| from typing import Optional | |
| import torch | |
| import numpy as np | |
| from librosa.filters import mel | |
| from .stft import STFT | |
| class MelSpectrogram(torch.nn.Module): | |
| def __init__( | |
| self, | |
| is_half: bool, | |
| n_mel_channels: int, | |
| sampling_rate: int, | |
| win_length: int, | |
| hop_length: int, | |
| n_fft: Optional[int] = None, | |
| mel_fmin: int = 0, | |
| mel_fmax: int = None, | |
| clamp: float = 1e-5, | |
| device=torch.device("cpu"), | |
| ): | |
| super().__init__() | |
| if n_fft is None: | |
| n_fft = win_length | |
| mel_basis = mel( | |
| sr=sampling_rate, | |
| n_fft=n_fft, | |
| n_mels=n_mel_channels, | |
| fmin=mel_fmin, | |
| fmax=mel_fmax, | |
| htk=True, | |
| ) | |
| mel_basis = torch.from_numpy(mel_basis).float() | |
| self.register_buffer("mel_basis", mel_basis) | |
| self.n_fft = n_fft | |
| self.hop_length = hop_length | |
| self.win_length = win_length | |
| self.clamp = clamp | |
| self.is_half = is_half | |
| self.stft = STFT( | |
| filter_length=n_fft, | |
| hop_length=hop_length, | |
| win_length=win_length, | |
| window="hann", | |
| use_torch_stft="privateuseone" not in str(device), | |
| ).to(device) | |
| def forward( | |
| self, | |
| audio: torch.Tensor, | |
| keyshift=0, | |
| speed=1, | |
| center=True, | |
| ): | |
| factor = 2 ** (keyshift / 12) | |
| win_length_new = int(np.round(self.win_length * factor)) | |
| magnitude = self.stft(audio, keyshift, speed, center) | |
| if keyshift != 0: | |
| size = self.n_fft // 2 + 1 | |
| resize = magnitude.size(1) | |
| if resize < size: | |
| magnitude = torch.nn.functional.pad(magnitude, (0, 0, 0, size - resize)) | |
| magnitude = magnitude[:, :size, :] * self.win_length / win_length_new | |
| mel_output = torch.matmul(self.mel_basis, magnitude) | |
| if self.is_half: | |
| mel_output = mel_output.half() | |
| log_mel_spec = torch.log(torch.clamp(mel_output, min=self.clamp)) | |
| return log_mel_spec | |